#!/usr/bin/env python
# coding: utf-8

# In[ ]:


get_ipython().run_line_magic('matplotlib', 'inline')


# 
# What is PyTorch?
# ================
# 
# It’s a Python based scientific computing package targeted at two sets of
# audiences:
# 
# -  A replacement for NumPy to use the power of GPUs
# -  a deep learning research platform that provides maximum flexibility
#    and speed
# 
# Getting Started
# ---------------
# 
# Tensors
# ^^^^^^^
# 
# Tensors are similar to NumPy’s ndarrays, with the addition being that
# Tensors can also be used on a GPU to accelerate computing.
# 
# 

# In[1]:


from __future__ import print_function
import torch


# Construct a 5x3 matrix, uninitialized:
# 
# 

# In[19]:


x = torch.empty(5, 3)
print(x)
x.shape


# Construct a randomly initialized matrix:
# 
# 

# In[3]:


x = torch.rand(5, 3)
print(x)


# Construct a matrix filled zeros and of dtype long:
# 
# 

# In[4]:


x = torch.zeros(5, 3, dtype=torch.long)
print(x)


# Construct a tensor directly from data:
# 
# 

# In[5]:


x = torch.tensor([5.5, 3])
print(x)


# or create a tensor basing on existing tensor. These methods
# will reuse properties of the input tensor, e.g. dtype, unless
# new values are provided by user
# 
# 

# In[6]:


x = x.new_ones(5, 3, dtype=torch.double)      # new_* methods take in sizes
print(x)

x = torch.randn_like(x, dtype=torch.float)    # override dtype!
print(x)                                      # result has the same size


# Get its size:
# 
# 

# In[7]:


print(x.size())


# <div class="alert alert-info"><h4>Note</h4><p>``torch.Size`` is in fact a tuple, so it supports all tuple operations.</p></div>
# 
# Operations
# ^^^^^^^^^^
# There are multiple syntaxes for operations. In the following
# example, we will take a look at the addition operation.
# 
# Addition: syntax 1
# 
# 

# In[8]:


y = torch.rand(5, 3)
print(x + y)


# Addition: syntax 2
# 
# 

# In[9]:


print(torch.add(x, y))


# Addition: providing an output tensor as argument
# 
# 

# In[10]:


result = torch.empty(5, 3)
torch.add(x, y, out=result)
print(result)


# Addition: in-place
# 
# 

# In[11]:


# adds x to y
y.add_(x)
print(y)


# <div class="alert alert-info"><h4>Note</h4><p>Any operation that mutates a tensor in-place is post-fixed with an ``_``.
#     For example: ``x.copy_(y)``, ``x.t_()``, will change ``x``.</p></div>
# 
# You can use standard NumPy-like indexing with all bells and whistles!
# 
# 

# In[12]:


print(x[:, 1])


# Resizing: If you want to resize/reshape tensor, you can use ``torch.view``:
# 
# 

# In[ ]:


x = torch.randn(4, 4)
y = x.view(16)
z = x.view(-1, 8)  # the size -1 is inferred from other dimensions
print(x.size(), y.size(), z.size())


# If you have a one element tensor, use ``.item()`` to get the value as a
# Python number
# 
# 

# In[ ]:


x = torch.randn(1)
print(x)
print(x.item())


# **Read later:**
# 
# 
#   100+ Tensor operations, including transposing, indexing, slicing,
#   mathematical operations, linear algebra, random numbers, etc.,
#   are described
#   `here <http://pytorch.org/docs/torch>`_.
# 
# NumPy Bridge
# ------------
# 
# Converting a Torch Tensor to a NumPy array and vice versa is a breeze.
# 
# The Torch Tensor and NumPy array will share their underlying memory
# locations, and changing one will change the other.
# 
# Converting a Torch Tensor to a NumPy Array
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# 
# 

# In[ ]:


a = torch.ones(5)
print(a)


# In[ ]:


b = a.numpy()
print(b)


# See how the numpy array changed in value.
# 
# 

# In[ ]:


a.add_(1)
print(a)
print(b)


# Converting NumPy Array to Torch Tensor
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# See how changing the np array changed the Torch Tensor automatically
# 
# 

# In[ ]:


import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
np.add(a, 1, out=a)
print(a)
print(b)


# All the Tensors on the CPU except a CharTensor support converting to
# NumPy and back.
# 
# CUDA Tensors
# ------------
# 
# Tensors can be moved onto any device using the ``.to`` method.
# 
# 

# In[ ]:


# let us run this cell only if CUDA is available
# We will use ``torch.device`` objects to move tensors in and out of GPU
if torch.cuda.is_available():
    device = torch.device("cuda")          # a CUDA device object
    y = torch.ones_like(x, device=device)  # directly create a tensor on GPU
    x = x.to(device)                       # or just use strings ``.to("cuda")``
    z = x + y
    print(z)
    print(z.to("cpu", torch.double))       # ``.to`` can also change dtype together!

